Introduction to R
Jan Vandepitte
30 August 2018
0. What is R
- Open source Script language for statistical programming
- data preparation, machine learning, experimentation, visualization (presentation, notebook, shiny dashboard)
- packaging system (CRAN) like nuget, npm
x <- 1
x + 1
[1] 2
0.1 Origins in LISP
LISP -> Scheme -> S -> R (cfr Ecmascript)
- REPL
- lambda's
- reflection (code is data)
- dynamic (if it quacks like a duck)
- abstract away underlying system (for domain experts)
- backed by fast C++, C or Fortran libraries
column oriented data structure (APL influence)
0.2 Origins in LISP (demo)
myF <- function(x) { x+1 }
myF
function(x) { x+1 }
myF(1)
[1] 2
apply(matrix(c(1,2,3,4),2,2),1,myF)
[,1] [,2]
[1,] 2 3
[2,] 4 5
1. Why R
- AI coming out of winter (pit of dispair)
- Omnipresent in AI (Skill like SQL, Ecmascript)
- Open source, Eco-system, History (de facto standard)
- different backends (citizen scientist, Big Data, Keras backend)
- Backed by and integrated in Microsoft products
1.1 Notable R tools in ecosystem
- packages: https://cran.r-project.org/
- IDE Rstudio: https://www.rstudio.com/
- notable R packages:
1.2 Checklist: Is R the tool for me right now?
- Do I want to use free (like beer) software : yes
- Do I want to experiment with different models from different creators : yes
- Does my data fit in main memory of my computer (no big data ): yes
- Doesn't my data fit in main memory of my computer : yes with some prerequisites
2. R in Microsoft products
- 2015 Microsoft buys Revolution Analytics
- R Integrated in several products
- https://mran.microsoft.com
2.1 Microsoft R Open (R extension)
Improved some pittfals of typical R distribution (single threaded, standardization of packages and models, object orientation…) CRAN
(see Typescript)
2.2 Microsoft Machine Learning Server (run time)
Client/Server Operationalizing R
- DeployR
- ScaleR
Now also python
2.3 Microsoft R Archive Network (MRAN)
Standardized package, snapshotting in time https://mran.microsoft.com/
2.4 R in SQL Server
Machine learning services in SQL Server
2.5 R in visual studio
2.6 R in Power BI
Visualization with R? PowerBIR?? (jk :p)
2.7 Azure machine learning
2.8 Azure HDInsight (hadoop as a service)
2.9 Azure Databricks (spark as a service)
R one of the languages on Apache Spark (besides Scala and Python)
2.10 In minecraft
2.11 Certification as a Data Scientist
3. Concepts
In the following segment I try to provide a high level run through of basic concepts of AI, machine learning and statistics
3.1 AI
3.2 DIKW pyramid
3.3 DIKW cycle
3.4 Statistics and Machine learning
3.5 Error terms
In programming we try to reduce the errors in our models (programs) by fixing bugs and doing unit testing.
Reduce Errors Of Our statistical models : Stochastical element. Error is quantifiable with data.
3.5 Error terms
Model is estimation of errorterms of data around model
3.5 Error terms
Error has a probability distribution around: assumed to be normal distributed
3.6 Error types : hypothesis testing
3.6 Error types : hypothesis testing
- type I : incorrectly detect effect when there is none (bias, noise…) : overfitting
- type II : incorrectly detect no effect (0 hypothesis) when there is an effect : underfitting
Programming = mathematical proof testen (https://en.wikipedia.org/wiki/Curry%E2%80%93Howard_correspondence)
3.7 Information = Variance
we train our model and test/validate it on the same data
3.7 Information = Variance
How related is our training, test our validation data subset? Idempotency of our model.
3.8 Big data - Central Limit Theorem
Larger sample size: more normal distribution
with more data, we need less assumptions of underlying error term
3.9 Big data - Power increases with sample size
Power is chance that our model will correctly detect an effect. Power will decrease with more features in our model. Power will increase with larger sample size for our model. More power = less underfitting (less type II error).
Median vs arithmetic average.
3.10 Big data - Data is the new oil
Conclusion: the more (quality) data, the better for our models
New headaches: Horizontal scalability, distributed systems, CAP and CALM theoreum …
3.11 Deep learning
deep neural networks, long history, more data and faster hardware (GPU and even TPU)
When in doubt use brute force ~ Ken Thompson
3.12 Deep learning
Renewed interest so some interesting developments
- Auto-encoder (encoder-decoder chained learned with internal representation)
- SEQ2SEQ (arbitrary size representation for e.g. NLP)
- Representation learning (less feature engineering)
- Auto-ML : deep learning for non-experts
- cross platform: tensorflow on RPI and in browser
- Theoretical framework (category theory): a new way of programming but with linear algebra http://colah.github.io/posts/2015-09-NN-Types-FP/
3.13 Deep learning
Deep learning est mort, vive differentiable programming ~ Yann LeCun - Chief AI facebook
https://medium.com/@karpathy/software-2-0-a64152b37c35 https://www.facebook.com/yann.lecun/posts/10155003011462143 https://techburst.io/deep-learning-est-mort-vive-differentiable-programming-5060d3c55074
3.14 Deep dive theoretical
3.15 Level I in Causal hierarchy
3.16 Simpson paradox
body weight related to disease (men vs women, children vs adults)
4. R for the .NET programmer
In knitR it's also possible to run code from other languages:
- https://yihui.name/knitr/demo/engines/
- http://datadrivensecurity.info/blog/posts/2015/Jun/running-other-languages-in-r-markdown-files/
So let's add an engine for .net
eng_dotnet <- function(options) {
# create a temporary file
f <- basename(tempfile("dotnet", '.', paste('.', "dotnet", sep = '')))
on.exit(unlink(f)) # cleanup temp file on function exit
writeLines(options$code, f)
out <- ''
# if eval != FALSE compile/run the code, preserving output
if (options$eval) {
out <- system(sprintf('dotnet script %s', paste(f, options$engine.opts)), intern=TRUE)
}
# spit back stuff to the user
engine_output(options, options$code, out)
}
knitr::knit_engines$set(dotnet=eng_dotnet)
4.0 Let's try out our R-package
if (!requireNamespace("devtools", quietly = TRUE)) { install.packages("devtools") }
devtools::install_github("yenwel/Rpresdotnetengine", force=T)
library(Rpresdotnetengine)
knitr::knit_engines$set(dotnet=eng_dotnet)
4.1 Let's try out our new engine
var i = 1;
i++;
Console.WriteLine(i++);
Console.WriteLine(++i);
Console.WriteLine(i--);
Console.WriteLine(--i);
2
4
4
2
4.2 Basic types (.NET)
Console.WriteLine(1);
Console.WriteLine(true);
Console.WriteLine("hello world?");
enum Color {Red, Green, Blue};
Console.WriteLine(Color.Red);
1
True
hello world?
Red
4.2 Basic types (R)
1
[1] 1
2.0
[1] 2
T
[1] TRUE
'hello world?'
[1] "hello world?"
as.factor(c('Red','Green','Blue'))[1];
[1] Red
Levels: Blue Green Red
as.ordered(c('Best','Bester','Bestest'))[1];
[1] Best
Levels: Best < Bester < Bestest
4.3 Collections and composite types (.NET)
Console.WriteLine(new [] { 1 , 2 , 3}[1]);
Console.WriteLine(new List<object> { "Fred" , 20}[0]);
Console.WriteLine(new Dictionary<string,object>{{"name","Fred"},{"age",20}}["name"]);
Console.WriteLine(new { Name = "Fred", Age = 20});
2
Fred
Fred
{ Name = Fred, Age = 20 }
4.3 Collections and composite types (R)
c(1,2,3)
[1] 1 2 3
matrix(1:9, nrow=3,ncol=3)
[,1] [,2] [,3]
[1,] 1 4 7
[2,] 2 5 8
[3,] 3 6 9
list(name="Fred", age=20)
$name
[1] "Fred"
$age
[1] 20
# R has at least three ways to do OO (S3, S4, Reference class) but don't bother do FP rather
4.3 Collections and composite types (R)
array(1:16,dim = c(2,2,2,2))[,,2,2]
[,1] [,2]
[1,] 13 15
[2,] 14 16
data.frame(name = c("Buddy", "Lisa"), age = c(10, 38), sex = as.factor(c("m","f")))
name age sex
1 Buddy 10 m
2 Lisa 38 f
4.4 Functions (.NET)
Func<int,int> myF = (int x) => x + 1;
Console.WriteLine(myF);
Console.WriteLine(myF(1));
Console.WriteLine(new [] {1 , 2 , 3 , 4}.Select(myF).FirstOrDefault());
System.Func`2[System.Int32,System.Int32]
2
2
4.4 Functions (R)
myF <- function(x) { x+1 }
myF
function(x) { x+1 }
myF(1)
[1] 2
apply(matrix(c(1,2,3,4),2,2),1,myF)
[,1] [,2]
[1,] 2 3
[2,] 4 5
4.5 Deep dive into R
start here:
then google (CRAN because R is to confusing for google)
??something
5. Demo's
- supply chain analysis :
- shiny app connecting to database: https://github.com/yenwel/shinyDatabaseExplorer
- analyse load tests (connect to db): https://github.com/yenwel/analyse-neustar-loadtest
- process IIS Url Rewrite xml: https://github.com/yenwel/processsUrlRewrite
- this presentation: https://github.com/yenwel/R-presentation
- An R-package for the knitr dotnet engine: https://github.com/yenwel/Rpresdotnetengine
5.1 Shiny App
5.2 data mining bitcoin and twitter
#http://beautifuldata.net/2015/01/querying-the-bitcoin-blockchain-with-r/
library(Rbitcoin)
trades <- market.api.process('kraken',c('BTC','EUR'),'trades')
Rbitcoin.plot(trades, col='blue')
5.2 data mining bitcoin and twitter
## load rtweet package
library(rtweet)
# these environment variables are set via an app you can make at https://developer.twitter.com/en/apps
envvar <- Sys.getenv(c("TWT_R_APP", "TWT_R_API", "TWT_R_SECRET","TWT_R_XS_TOKEN","TWT_R_XS_SECRET"))
appname <- envvar[1]
key <- envvar[2]
secret <- envvar[3]
access_token <- envvar[4]
access_secret <- envvar[5]
## authenticate via access token
token <- create_token(
app = appname,
consumer_key = key,
consumer_secret = secret,
access_token = access_token,
access_secret = access_secret)
bitcoin_tweets <- search_tweets(q = "#bitcoin", n = 15000 , retryonratelimit=F)
5.2 datamining bitcoin and twitter
str(trades)
List of 6
$ market : chr "kraken"
$ base : chr "BTC"
$ quote : chr "EUR"
$ timestamp : POSIXct[1:1], format: "2018-08-30 08:31:04"
$ market_timestamp: POSIXct[1:1], format: NA
$ trades :Classes 'data.table' and 'data.frame': 1000 obs. of 5 variables:
..$ date : POSIXct[1:1000], format: "2018-08-30 04:32:47" ...
..$ price : num [1:1000] 5988 5988 5987 5987 5987 ...
..$ amount: num [1:1000] 0.002461 0.079674 0.045852 0.000118 0.245638 ...
..$ tid : chr [1:1000] NA NA NA NA ...
..$ type : chr [1:1000] "bid" "bid" "ask" "ask" ...
..- attr(*, ".internal.selfref")=<externalptr>
5.2 datamining bitcoin and twitter
summary(trades)
Length Class Mode
market 1 -none- character
base 1 -none- character
quote 1 -none- character
timestamp 1 POSIXct numeric
market_timestamp 1 POSIXct numeric
trades 5 data.table list
5.2 datamining bitcoin and twitter
summary(trades$trades)
date price amount
Min. :2018-08-30 04:32:47 Min. :5931 Min. :0.000000
1st Qu.:2018-08-30 05:19:12 1st Qu.:5957 1st Qu.:0.003941
Median :2018-08-30 05:49:43 Median :5968 Median :0.024570
Mean :2018-08-30 05:40:45 Mean :5966 Mean :0.162106
3rd Qu.:2018-08-30 06:02:27 3rd Qu.:5977 3rd Qu.:0.100000
Max. :2018-08-30 06:31:02 Max. :5997 Max. :5.319069
tid type
Length:1000 Length:1000
Class :character Class :character
Mode :character Mode :character
5.2 datamining bitcoin and twitter
summary(trades$trades$date)
Min. 1st Qu. Median
"2018-08-30 04:32:47" "2018-08-30 05:19:12" "2018-08-30 05:49:43"
Mean 3rd Qu. Max.
"2018-08-30 05:40:45" "2018-08-30 06:02:27" "2018-08-30 06:31:02"
5.2 datamining bitcoin and twitter
str(bitcoin_tweets)
Classes 'tbl_df', 'tbl' and 'data.frame': 11996 obs. of 88 variables:
$ user_id : chr "910374976540340230" "1019517144831160322" "1019517144831160322" "954072093511114752" ...
$ status_id : chr "1035052207249313792" "1035052205764751360" "1035050117961789440" "1035052205634736128" ...
$ created_at : POSIXct, format: "2018-08-30 06:30:51" "2018-08-30 06:30:50" ...
$ screen_name : chr "Evileye420" "KucoinT" "KucoinT" "DASHTicker" ...
$ text : chr "Visit our website and find more information about the #MEMEZ project. \n\nDon't forget to share the news about "| __truncated__ "Asch (XAS) Deposit ve Withdraw Artik Etkin\n\n Asch (XAS) cüzdani için yükseltme tamamlandi ve artik normal ça"| __truncated__ "KuCoin Haftalik Raporu #16 – 30/08/2018\n\n En önemli güncellemelerimizi Medium’dan takip edin!\n\n Detayli b"| __truncated__ "#DASH Price is 0.02605342 (-0.00001105) #BTC / 182.635149027 (+0.28252) #USD. Market rank is 13. #dash #bitcoin #blockchain" ...
$ source : chr "Twitter Web Client" "Twitter Web Client" "Twitter Web Client" "Dashticker" ...
$ display_text_width : num 140 275 229 123 123 122 123 123 122 122 ...
$ reply_to_status_id : chr NA NA NA NA ...
$ reply_to_user_id : chr NA NA NA NA ...
$ reply_to_screen_name : chr NA NA NA NA ...
$ is_quote : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
$ is_retweet : logi TRUE FALSE FALSE FALSE FALSE FALSE ...
$ favorite_count : int 0 0 0 0 0 0 0 0 1 1 ...
$ retweet_count : int 280 0 0 0 0 0 0 0 0 0 ...
$ hashtags :List of 11996
..$ : chr "MEMEZ"
..$ : chr "bitcoin"
..$ : chr "KuCoin" "CryptoExchange" "bitcoin" "cryptocurrency" ...
..$ : chr "DASH" "BTC" "USD" "dash" ...
..$ : chr "DASH" "BTC" "USD" "dash" ...
..$ : chr "DASH" "BTC" "USD" "dash" ...
..$ : chr "DASH" "BTC" "USD" "dash" ...
..$ : chr "DASH" "BTC" "USD" "dash" ...
..$ : chr "DASH" "BTC" "USD" "dash" ...
..$ : chr "DASH" "BTC" "USD" "dash" ...
..$ : chr "DASH" "BTC" "USD" "dash" ...
..$ : chr "DASH" "BTC" "USD" "dash" ...
..$ : chr "DASH" "BTC" "USD" "dash" ...
..$ : chr "DASH" "BTC" "USD" "dash" ...
..$ : chr "DASH" "BTC" "USD" "dash" ...
..$ : chr "DASH" "BTC" "USD" "dash" ...
..$ : chr "DASH" "BTC" "USD" "dash" ...
..$ : chr "DASH" "BTC" "USD" "dash" ...
..$ : chr "DASH" "BTC" "USD" "dash" ...
..$ : chr "SUB" "BTC" "USD" "substratum" ...
..$ : chr "SUB" "BTC" "USD" "substratum" ...
..$ : chr "SUB" "BTC" "USD" "substratum" ...
..$ : chr "SUB" "BTC" "USD" "substratum" ...
..$ : chr "SUB" "BTC" "USD" "substratum" ...
..$ : chr "SUB" "BTC" "USD" "substratum" ...
..$ : chr "SUB" "BTC" "USD" "substratum" ...
..$ : chr "SUB" "BTC" "USD" "substratum" ...
..$ : chr "SUB" "BTC" "USD" "substratum" ...
..$ : chr "SUB" "BTC" "USD" "substratum" ...
..$ : chr "SUB" "BTC" "USD" "substratum" ...
..$ : chr "SUB" "BTC" "USD" "substratum" ...
..$ : chr "SUB" "BTC" "USD" "substratum" ...
..$ : chr "SUB" "BTC" "USD" "substratum" ...
..$ : chr "SUB" "BTC" "USD" "substratum" ...
..$ : chr "SUB" "BTC" "USD" "substratum" ...
..$ : chr "SUB" "BTC" "USD" "substratum" ...
..$ : chr "SUB" "BTC" "USD" "substratum" ...
..$ : chr "ADA" "BTC" "USD" "cardano" ...
..$ : chr "ADA" "BTC" "USD" "cardano" ...
..$ : chr "ADA" "BTC" "USD" "cardano" ...
..$ : chr "ADA" "BTC" "USD" "cardano" ...
..$ : chr "ADA" "BTC" "USD" "cardano" ...
..$ : chr "ADA" "BTC" "USD" "cardano" ...
..$ : chr "ADA" "BTC" "USD" "cardano" ...
..$ : chr "ADA" "BTC" "USD" "cardano" ...
..$ : chr "ADA" "BTC" "USD" "cardano" ...
..$ : chr "ADA" "BTC" "USD" "cardano" ...
..$ : chr "ADA" "BTC" "USD" "cardano" ...
..$ : chr "ADA" "BTC" "USD" "cardano" ...
..$ : chr "ADA" "BTC" "USD" "cardano" ...
..$ : chr "ADA" "BTC" "USD" "cardano" ...
..$ : chr "ADA" "BTC" "USD" "cardano" ...
..$ : chr "ADA" "BTC" "USD" "cardano" ...
..$ : chr "ADA" "BTC" "USD" "cardano" ...
..$ : chr "ADA" "BTC" "USD" "cardano" ...
..$ : chr "EOS" "BTC" "USD" "eos" ...
..$ : chr "EOS" "BTC" "USD" "eos" ...
..$ : chr "EOS" "BTC" "USD" "eos" ...
..$ : chr "EOS" "BTC" "USD" "eos" ...
..$ : chr "EOS" "BTC" "USD" "eos" ...
..$ : chr "EOS" "BTC" "USD" "eos" ...
..$ : chr "EOS" "BTC" "USD" "eos" ...
..$ : chr "EOS" "BTC" "USD" "eos" ...
..$ : chr "EOS" "BTC" "USD" "eos" ...
..$ : chr "EOS" "BTC" "USD" "eos" ...
..$ : chr "EOS" "BTC" "USD" "eos" ...
..$ : chr "EOS" "BTC" "USD" "eos" ...
..$ : chr "EOS" "BTC" "USD" "eos" ...
..$ : chr "EOS" "BTC" "USD" "eos" ...
..$ : chr "EOS" "BTC" "USD" "eos" ...
..$ : chr "EOS" "BTC" "USD" "eos" ...
..$ : chr "EOS" "BTC" "USD" "eos" ...
..$ : chr "EOS" "BTC" "USD" "eos" ...
..$ : chr "bitcoin" "elpreciodelbtc" "criptomonedas"
..$ : chr "bitcoin" "elpreciodelbtc" "criptomonedas"
..$ : chr "bitcoin" "elpreciodelbtc" "criptomonedas"
..$ : chr "bitcoin" "elpreciodelbtc" "criptomonedas"
..$ : chr "bitcoin" "elpreciodelbtc" "criptomonedas"
..$ : chr "bitcoin" "elpreciodelbtc" "criptomonedas"
..$ : chr "bitcoin" "elpreciodelbtc" "criptomonedas"
..$ : chr "bitcoin" "elpreciodelbtc" "criptomonedas"
..$ : chr "bitcoin" "elpreciodelbtc" "criptomonedas"
..$ : chr "XEM" "BTC" "USD" "nem" ...
..$ : chr "XEM" "BTC" "USD" "nem" ...
..$ : chr "XEM" "BTC" "USD" "nem" ...
..$ : chr "XEM" "BTC" "USD" "nem" ...
..$ : chr "XEM" "BTC" "USD" "nem" ...
..$ : chr "XEM" "BTC" "USD" "nem" ...
..$ : chr "XEM" "BTC" "USD" "nem" ...
..$ : chr "XEM" "BTC" "USD" "nem" ...
..$ : chr "XEM" "BTC" "USD" "nem" ...
..$ : chr "XEM" "BTC" "USD" "nem" ...
..$ : chr "XEM" "BTC" "USD" "nem" ...
..$ : chr "XEM" "BTC" "USD" "nem" ...
..$ : chr "XEM" "BTC" "USD" "nem" ...
..$ : chr "XEM" "BTC" "USD" "nem" ...
..$ : chr "XEM" "BTC" "USD" "nem" ...
..$ : chr "XEM" "BTC" "USD" "nem" ...
..$ : chr "FUNDChain" "airdrops" "airdrop"
.. [list output truncated]
$ symbols :List of 11996
..$ : chr NA
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.. [list output truncated]
$ urls_url :List of 11996
..$ : chr NA
..$ : chr "news.kucoin.com/en/asch-xas-de…"
..$ : chr "medium.com/kucoinexchange…"
..$ : chr NA
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.. [list output truncated]
$ urls_t.co :List of 11996
..$ : chr NA
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.. [list output truncated]
$ lang : chr "en" "tr" "tr" "en" ...
$ quoted_status_id : chr NA NA NA NA ...
$ quoted_text : chr NA NA NA NA ...
$ quoted_created_at : POSIXct, format: NA NA ...
$ quoted_source : chr NA NA NA NA ...
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$ quoted_retweet_count : int NA NA NA NA NA NA NA NA NA NA ...
$ quoted_user_id : chr NA NA NA NA ...
$ quoted_screen_name : chr NA NA NA NA ...
$ quoted_name : chr NA NA NA NA ...
$ quoted_followers_count : int NA NA NA NA NA NA NA NA NA NA ...
$ quoted_friends_count : int NA NA NA NA NA NA NA NA NA NA ...
$ quoted_statuses_count : int NA NA NA NA NA NA NA NA NA NA ...
$ quoted_location : chr NA NA NA NA ...
$ quoted_description : chr NA NA NA NA ...
$ quoted_verified : logi NA NA NA NA NA NA ...
$ retweet_status_id : chr "1021091935438671872" NA NA NA ...
$ retweet_text : chr "Visit our website and find more information about the #MEMEZ project. \n\nDon't forget to share the news about "| __truncated__ NA NA NA ...
$ retweet_created_at : POSIXct, format: "2018-07-22 17:57:43" NA ...
$ retweet_source : chr "Twitter Web Client" NA NA NA ...
$ retweet_favorite_count : int 265 NA NA NA NA NA NA NA NA NA ...
$ retweet_retweet_count : int 280 NA NA NA NA NA NA NA NA NA ...
$ retweet_user_id : chr "1019498101877469184" NA NA NA ...
$ retweet_screen_name : chr "MEMEZOFFICIAL" NA NA NA ...
$ retweet_name : chr "MEMEZ OFFICIAL" NA NA NA ...
$ retweet_followers_count: int 10768 NA NA NA NA NA NA NA NA NA ...
$ retweet_friends_count : int 35 NA NA NA NA NA NA NA NA NA ...
$ retweet_statuses_count : int 51 NA NA NA NA NA NA NA NA NA ...
$ retweet_location : chr "" NA NA NA ...
$ retweet_description : chr "The MEMEZ Token project will share the best memes in the crypto world and will reward all active members with FREE airdrops!" NA NA NA ...
$ retweet_verified : logi FALSE NA NA NA NA NA ...
$ place_url : chr NA NA NA NA ...
$ place_name : chr NA NA NA NA ...
$ place_full_name : chr NA NA NA NA ...
$ place_type : chr NA NA NA NA ...
$ country : chr NA NA NA NA ...
$ country_code : chr NA NA NA NA ...
$ geo_coords :List of 11996
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.. [list output truncated]
$ coords_coords :List of 11996
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.. [list output truncated]
$ bbox_coords :List of 11996
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.. [list output truncated]
$ status_url : chr "https://twitter.com/Evileye420/status/1035052207249313792" "https://twitter.com/KucoinT/status/1035052205764751360" "https://twitter.com/KucoinT/status/1035050117961789440" "https://twitter.com/DASHTicker/status/1035052205634736128" ...
$ name : chr "Sp Rahman" "KuCoin Exchange Turkey" "KuCoin Exchange Turkey" "Dash Price Ticker" ...
$ location : chr "Indonesia" "Istanbul, Türkiye" "Istanbul, Türkiye" "World Wide Web" ...
$ description : chr "Your Life, isn't your<U+0001F60D><U+0001F60D><U+0001F975><U+0001F976><U+0001F63F>\n\n#Follow #Followback #Bitco"| __truncated__ "https://t.co/TJQgwh37m5 Uluslararasi kripto para borsasi Türkiye resmi hesabi" "https://t.co/TJQgwh37m5 Uluslararasi kripto para borsasi Türkiye resmi hesabi" "Dash Price Details (every 30 minute)" ...
$ url : chr "https://t.co/OUD7taoIfC" "https://t.co/TJQgwh37m5" "https://t.co/TJQgwh37m5" "https://t.co/4a2BZvcRvW" ...
$ protected : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
$ followers_count : int 3054 76 76 115 115 115 115 115 115 115 ...
$ friends_count : int 4330 3 3 6 6 6 6 6 6 6 ...
$ listed_count : int 4 0 0 7 7 7 7 7 7 7 ...
$ statuses_count : int 260 144 144 9995 9995 9995 9995 9995 9995 9995 ...
$ favourites_count : int 1977 11 11 0 0 0 0 0 0 0 ...
$ account_created_at : POSIXct, format: "2017-09-20 05:28:03" "2018-07-18 09:40:03" ...
$ verified : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
$ profile_url : chr "https://t.co/OUD7taoIfC" "https://t.co/TJQgwh37m5" "https://t.co/TJQgwh37m5" "https://t.co/4a2BZvcRvW" ...
$ profile_expanded_url : chr "http://BITCOINTALK.ORG" "http://kucoin.com" "http://kucoin.com" "http://dash.org" ...
$ account_lang : chr "en" "en" "en" "en" ...
$ profile_banner_url : chr "https://pbs.twimg.com/profile_banners/910374976540340230/1511711346" "https://pbs.twimg.com/profile_banners/1019517144831160322/1533929597" "https://pbs.twimg.com/profile_banners/1019517144831160322/1533929597" "https://pbs.twimg.com/profile_banners/954072093511114752/1517502469" ...
$ profile_background_url : chr "http://abs.twimg.com/images/themes/theme1/bg.png" NA NA NA ...
$ profile_image_url : chr "http://pbs.twimg.com/profile_images/1027605051903102976/wpWitHTI_normal.jpg" "http://pbs.twimg.com/profile_images/1027199379038498817/SrXmyNWD_normal.jpg" "http://pbs.twimg.com/profile_images/1027199379038498817/SrXmyNWD_normal.jpg" "http://pbs.twimg.com/profile_images/959100729863626752/11aUMndj_normal.jpg" ...
5.2 datamining bitcoin and twitter
summary(bitcoin_tweets)
user_id status_id created_at
Length:11996 Length:11996 Min. :2018-08-29 21:59:52
Class :character Class :character 1st Qu.:2018-08-30 00:03:52
Mode :character Mode :character Median :2018-08-30 02:24:39
Mean :2018-08-30 02:18:23
3rd Qu.:2018-08-30 04:27:29
Max. :2018-08-30 06:30:51
screen_name text source
Length:11996 Length:11996 Length:11996
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
display_text_width reply_to_status_id reply_to_user_id
Min. : 8.0 Length:11996 Length:11996
1st Qu.:134.0 Class :character Class :character
Median :140.0 Mode :character Mode :character
Mean :146.1
3rd Qu.:140.0
Max. :298.0
reply_to_screen_name is_quote is_retweet favorite_count
Length:11996 Mode :logical Mode :logical Min. : 0.0000
Class :character FALSE:11521 FALSE:4449 1st Qu.: 0.0000
Mode :character TRUE :475 TRUE :7547 Median : 0.0000
Mean : 0.5904
3rd Qu.: 0.0000
Max. :405.0000
retweet_count hashtags.Length hashtags.Class hashtags.Mode
Min. : 0.0 1 -none- character
1st Qu.: 0.0 1 -none- character
Median : 6.0 6 -none- character
Mean : 483.9 6 -none- character
3rd Qu.: 176.0 6 -none- character
Max. :193565.0 6 -none- character
6 -none- character
6 -none- character
6 -none- character
6 -none- character
6 -none- character
6 -none- character
6 -none- character
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3 -none- character
3 -none- character
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3 -none- character
3 -none- character
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1 -none- character
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1 -none- character
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quoted_created_at quoted_source quoted_favorite_count
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1st Qu.:2018-08-27 18:10:09 Class :character 1st Qu.: 56.5
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status_url name location
Length:11996 Length:11996 Length:11996
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
description url protected followers_count
Length:11996 Length:11996 Mode :logical Min. : 0
Class :character Class :character FALSE:11996 1st Qu.: 109
Mode :character Mode :character Median : 495
Mean : 4646
3rd Qu.: 2982
Max. :517328
friends_count listed_count statuses_count favourites_count
Min. : 0.0 Min. : 0.0 Min. : 1 Min. : 0
1st Qu.: 176.8 1st Qu.: 0.0 1st Qu.: 481 1st Qu.: 83
Median : 593.0 Median : 3.0 Median : 2094 Median : 558
Mean : 3163.2 Mean : 160.7 Mean : 22769 Mean : 6110
3rd Qu.: 2331.2 3rd Qu.: 18.0 3rd Qu.: 9895 3rd Qu.: 3137
Max. :481732.0 Max. :20580.0 Max. :1756077 Max. :685592
account_created_at verified profile_url
Min. :2006-11-18 06:51:03 Mode :logical Length:11996
1st Qu.:2014-04-05 06:50:58 FALSE:11964 Class :character
Median :2017-08-19 16:28:57 TRUE :32 Mode :character
Mean :2016-01-01 23:39:35
3rd Qu.:2018-03-04 19:29:05
Max. :2018-08-30 06:13:28
profile_expanded_url account_lang profile_banner_url
Length:11996 Length:11996 Length:11996
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
profile_background_url profile_image_url
Length:11996 Length:11996
Class :character Class :character
Mode :character Mode :character
[ reached getOption("max.print") -- omitted 10860 rows ]
5.2 datamining bitcoin and twitter
summary(bitcoin_tweets$created_at)
Min. 1st Qu. Median
"2018-08-29 21:59:52" "2018-08-30 00:03:52" "2018-08-30 02:24:39"
Mean 3rd Qu. Max.
"2018-08-30 02:18:23" "2018-08-30 04:27:29" "2018-08-30 06:30:51"
5.2 datamining bitcoin and twitter
mintime <-max(min(trades$trades$date),min(bitcoin_tweets$created_at))
maxtime <- min(max(bitcoin_tweets$created_at),max(trades$trades$date))
tradesinrange <- trades$trades[trades$trades$date >= mintime & trades$trades$date <= maxtime,]
tweetsinrange <- bitcoin_tweets[bitcoin_tweets$created_at >= mintime & bitcoin_tweets$created_at <= maxtime,]
mintime
[1] "2018-08-30 04:32:47 UTC"
maxtime
[1] "2018-08-30 06:30:51 UTC"
summary(tradesinrange$date)
Min. 1st Qu. Median
"2018-08-30 04:32:47" "2018-08-30 05:19:09" "2018-08-30 05:49:35"
Mean 3rd Qu. Max.
"2018-08-30 05:40:36" "2018-08-30 06:02:25" "2018-08-30 06:30:49"
summary(tweetsinrange$created_at)
Min. 1st Qu. Median
"2018-08-30 04:32:49" "2018-08-30 05:04:53" "2018-08-30 05:34:39"
Mean 3rd Qu. Max.
"2018-08-30 05:34:01" "2018-08-30 06:02:02" "2018-08-30 06:30:51"
5.2 datamining bitcoin and twitter
hist(tradesinrange$date, "mins")
5.2 datamining bitcoin and twitter
hist(tweetsinrange$created_at, "mins")
5.2 datamining bitcoin and twitter
#https://ro-che.info/articles/2017-02-22-group_by_month_r
#https://stackoverflow.com/questions/23528862/summarize-with-conditions-in-dplyr
library(dplyr)
library(lubridate)
tradessummary <- tradesinrange %>%
group_by(min=floor_date(date, "minute")) %>%
summarize(askamount = sum(amount[type=="ask"]),
bidamount = sum(amount[type=="bid"]),
askprice = median(price[type=="ask"]),
bidprice = median(price[type=="bid"]))
tweetssummary <- tweetsinrange %>%
count(min=floor_date(created_at, "minute"))
5.2 datamining bitcoin and twitter
tradesandtweets <- tradessummary %>% inner_join(tweetssummary)
colnames(tradesandtweets)[1] <- "minute"
colnames(tradesandtweets)[6] <- "tweets"
summary(tradesandtweets)
minute askamount bidamount
Min. :2018-08-30 04:32:00 Min. : 0.0000 Min. : 0.00000
1st Qu.:2018-08-30 05:05:45 1st Qu.: 0.0000 1st Qu.: 0.01512
Median :2018-08-30 05:33:30 Median : 0.0513 Median : 0.09965
Mean :2018-08-30 05:33:11 Mean : 0.7873 Mean : 0.65563
3rd Qu.:2018-08-30 06:02:15 3rd Qu.: 0.5045 3rd Qu.: 0.37287
Max. :2018-08-30 06:30:00 Max. :30.7249 Max. :17.25619
askprice bidprice tweets
Min. :5935 Min. :5935 Min. : 7.00
1st Qu.:5962 1st Qu.:5964 1st Qu.:20.00
Median :5968 Median :5971 Median :23.00
Mean :5969 Mean :5972 Mean :24.16
3rd Qu.:5978 3rd Qu.:5983 3rd Qu.:26.00
Max. :5992 Max. :5993 Max. :98.00
NA's :31 NA's :4
5.2 datamining bitcoin and twitter
library(zoo)
tradesandtweetsTS <- zoo(tradesandtweets[2:6],tradesandtweets$minute)
tradesandtweetsTSImputed <- na.locf(na.locf(tradesandtweetsTS),fromLast = TRUE)
5.2 datamining bitcoin and twitter
summary(tradesandtweetsTSImputed)
Index askamount bidamount
Min. :2018-08-30 04:32:00 Min. : 0.0000 Min. : 0.00000
1st Qu.:2018-08-30 05:05:45 1st Qu.: 0.0000 1st Qu.: 0.01512
Median :2018-08-30 05:33:30 Median : 0.0513 Median : 0.09965
Mean :2018-08-30 05:33:11 Mean : 0.7873 Mean : 0.65563
3rd Qu.:2018-08-30 06:02:15 3rd Qu.: 0.5045 3rd Qu.: 0.37287
Max. :2018-08-30 06:30:00 Max. :30.7249 Max. :17.25619
askprice bidprice tweets
Min. :5935 Min. :5935 Min. : 7.00
1st Qu.:5963 1st Qu.:5964 1st Qu.:20.00
Median :5971 Median :5971 Median :23.00
Mean :5971 Mean :5972 Mean :24.16
3rd Qu.:5980 3rd Qu.:5982 3rd Qu.:26.00
Max. :5992 Max. :5993 Max. :98.00
5.2 datamining bitcoin and twitter
plot(tradesandtweetsTSImputed)
5.2 datamining bitcoin and twitter
library(lmtest)
grangertest(tweets ~ bidprice, order = 3, data=tradesandtweetsTSImputed)
Granger causality test
Model 1: tweets ~ Lags(tweets, 1:3) + Lags(bidprice, 1:3)
Model 2: tweets ~ Lags(tweets, 1:3)
Res.Df Df F Pr(>F)
1 102
2 105 -3 0.7906 0.5019
grangertest(tweets ~ askprice, order = 3, data=tradesandtweetsTSImputed)
Granger causality test
Model 1: tweets ~ Lags(tweets, 1:3) + Lags(askprice, 1:3)
Model 2: tweets ~ Lags(tweets, 1:3)
Res.Df Df F Pr(>F)
1 102
2 105 -3 1.2985 0.2791
5.2 datamining bitcoin and twitter
library(lmtest)
grangertest(bidprice ~ tweets, order = 3, data=tradesandtweetsTSImputed)
Granger causality test
Model 1: bidprice ~ Lags(bidprice, 1:3) + Lags(tweets, 1:3)
Model 2: bidprice ~ Lags(bidprice, 1:3)
Res.Df Df F Pr(>F)
1 102
2 105 -3 2.3425 0.07755 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
grangertest(askprice ~ tweets, order = 3, data=tradesandtweetsTSImputed)
Granger causality test
Model 1: askprice ~ Lags(askprice, 1:3) + Lags(tweets, 1:3)
Model 2: askprice ~ Lags(askprice, 1:3)
Res.Df Df F Pr(>F)
1 102
2 105 -3 4.0197 0.009512 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1